Received December 13 2012, Revised February 15 2013, Accepted April 11 2013
Copyright ⵑ 2013 by the Korean Society of Civil Engineers
ǣǣȀȀǤǤȀͳͲǤͳʹͷʹȀ ǤʹͲͳ͵Ǥ͵͵ǤͶǤͳͷͺͳ Ǥ ǤǤ
⡷ᇛ#⟖ⱒ#㣦㛲ኺ#⮫⛶ፊⴖ#㝞⽾ⴲⱧ#㡷⛯ⴂ#⇖⮿㬚#ᦂ⢚㉞ᦂ⮫#㉦ᨎ⢚ᇂ#
⾏⻏#♮ⷓ##0#⛚ⱶ⢚Ἲ#ᢾ♿⳺Ḛ#0
ୋনผ ȵต ȵଲ ȵܛָ
Jang, Sunghoon*, Kim, Hyo-Seung**, Lee, Chungwon***, Kim, Dong-Kyu****
Calculation of the Peak-hour Ratio at Urban Railway Stations Reflecting Passenger Demand Pattern and Land Use Inventory - A Case of Seoul -
ABSTRACT
The aim of this study is to suggest a methodology for calculating the peak-hour ratio of passengers at urban railway stations by reflecting the characteristics of passenger demand patterns and the land use inventory of stations. To achieve this, urban railway stations in Seoul are divided into three groups by using factor analysis and cluster analysis. For each station group, we calculate five and four variables related to the passenger demand patterns and the land use inventory of stations, respectively, as well as the peak-hour ratios of passengers. Among these nine variables, average daily passengers and the location quotient (LQ) index for business services are selected as the classification criteria for station groups based on statistical tests. Using the two variables, a group allocation process is suggested to estimate the peak-hour ratio of passengers for a newly-constructed station. Evaluation results based on thirteen stations show that the proposed methodology produces lower errors than the currently-used guideline does. The results of this study contribute to establishing efficiently construction and operation plans for newly-constructed stations.
Key words : Peak-hour ratio, Passenger demand pattern, Land use inventory, Clustering analysis, Group allocation process
Ⅹಾ
ᅙᩑǍ۵ᩎᔍ᮹~ᙹ➉▕ŝ☁ḡᯕᬊ✚ᖒᮥၹᩢ⦹ᩍࠥ℁ࠥᩎ~᮹℉ࢱeḲᵲශᮥᔑᱶ⦹۵ႊჶುᮥᱽ⦹۵äᮥ༊ᱢᮝ ಽ⦽݅. ᯕෝ᭥⦹ᩍᯙᇥᕾŝǑḲᇥᕾᮥʑၹᮝಽᕽᬙࠥ℁ࠥᩎᔍෝ3}᮹ə൚ᮝಽᇥඹ⦽݅. bə൚ᄥಽᯕᬊᙹ᮹℉ࢱeḲ ᵲශŝ⧉̹ࠥ℁ࠥ~ᙹ➉▕ŝᩎᔍ᮹☁ḡᯕᬊŝšಉ⦽5aḡᄡᙹ᪡4aḡᄡᙹෝbbᔑ⇽⦽݅. ☖ĥáᱶᮥʑၹᮝಽ, ᯕᄡᙹॅ
ᵲᯝ⠪Ɂ~ᙹ᪡ᨦྕLQḡᙹaᩎᔍ᮹ə൚ᮥǍᇥ⦹۵ʑᵡᮝಽᖁᱶࡽ݅. ࢱᄡᙹॅᮥ⪽ᬊ⦹ᩍᔩಽÕᖅࡹ۵ࠥ℁ࠥᩎ᮹℉ࢱe
Ḳᵲශᮥ⇵ᱶ⦹ʑ᭥⦽ə൚⧁ݚŝᱶᮥᱽ⦽݅. 13}᮹ࠥ℁ࠥᩎᯱഭෝᯕᬊ⦽á᷾ᮥ☖⧕ᅙᩑǍ᮹ႊჶುᮡ⩥ᰍᯕᬊࡹ۵ḡ⋉ᨱእ
⦹ᩍᱢᮡ᪅₉ෝᔑ⇽⧁ᙹᯩ݅۵äᮥ⪶ᯙ⦹ᩡ݅. ᅙᩑǍ᮹ႊჶುᮡᝁȽ℁ࠥᩎᔍ᮹⬉ᮉᱢᯙÕᖅၰᬕᩢĥ⫮ᙹพᨱʑᩍ⧁ᙹᯩᮥ
äᮝಽᔍഭࡽ݅.
áᔪᨕ ℉ࢱeḲᵲශ, ~ᙹ➉▕, ☁ḡᯕᬊ, ǑḲᇥᕾ, ə൚⧁ݚŝᱶ
İࣀėॡ
Fig. 1. Flow of Research
1. ᕽು
1.1 ֜ଭࢼլࢫࡧୡ
℁ࠥᩎ ~ᙹ᮹ ℉ࢱe Ḳᵲශᮡ ℁ࠥᖅ᮹ ĥ⫮ ၰ
ᬕᩢᨱᵲ⦽ᗭಽ⪽ᬊࡹŁᯩ݅. ℉ࢱeḲᵲශᮥʑᵡᮝಽ
ĥ݉ᯕӹݡʑŖe, ᅕ⧪☖ಽ᪡zᮡʑ᳕᮹ᩎᔍᖅᮥᖅĥ⦹
Ł(Korea Rail Network Authority, 2010a), ⪹ᖝ░ᖅ᮹ᕽእ ᜅᙹᵡᇥᕾᨱᔍᬊ⦹ᩍ⩥⫊ᇥᕾၰ⨆⬥ᬕᩢĥ⫮ᮥษಉ⦽݅
(Ministry of Land, Transport and Maritime Affairs, 2010).
ࠥ℁ࠥ᮹ ℉ࢱe Ḳᵲශᮡ ݅ᮭŝ zᮡ ✚Ḷᮥ w۵݅.
ຝᱡ, ⋕Łෝݡᔢᮝಽ⦽ʑ᳕ᩑǍᨱ໕, ᬊ₉ᨱእ⦹ᩍ
ݡᵲƱ☖᮹℉ࢱeḲᵲශᯕ׳ᮝ໑, ✚⯩Ųᩎ℁ࠥ᮹Ğᬑ
℉ࢱeḲᵲශᯕ᧞25%, ࠥ℁ࠥ᮹Ğᬑ᧞17%᮹sᮥw۵
॒ ℁ࠥ᮹ ℉ࢱe Ḳᵲශᯕ ׳ᮡ äᮝಽ ᳑ᔍࡹᨩ݅(Vuchic, 2005 ᰍᯙᬊ). ੱ⦽, ᯝၹᱢᮝಽᵝÑḡǍᨱᕽᨦྕḡǍಽ᮹☖⧪
ᮡ᪅ᱥ℉ࢱ, ၹݡႊ⨆ᮡ᪅⬥℉ࢱᨱḲᵲ⦹۵Ğ⨆ᮥᅕᯕ໑, ᔢᨦḡǍ᮹Ğᬑᔢݡᱢᮝಽ℉ࢱeḲᵲශᯕԏᮡĞ⨆ᮥᅕᯙ
݅(Black, 1995).
⩥ᰍᬑญӹᨱᕽᝁȽ℁ࠥᩎᔍෝÕᖅ⧁ভᩎᔍෝᯕᬊ⧁
~ᙹ᮹℉ࢱeḲᵲශᮡḡ⋉॒ᨱ⪶⯩ᱽࡹᨕᯩḡᦫᮝ ໑, ᩑǍᯱ᮹ᯱᮉᱢᯙ❱݉ᨱɝÑ⦹ᩍəsᮥᱢᬊ⦹Łᯩ݅.
ᕽᩎᔍෝᯕᬊ⦹۵℉ࢱe~ᙹෝᱶ⪶⯩⇵ᱶ⦹۵ߑᨱ
ᨕಅᬡᯕᯩŁ, ᯕ۵ᝁȽᩎᔍ᮹Ƚ༉ᔑᱶᨱྙᱽෝʑ┉݅.
ᅙᩑǍ۵ᝁȽᩎᔍÕᖅ, ࠥ℁ࠥᩎᮥᯕᬊ⦹۵~ᙹ᮹
℉ࢱeḲᵲශᮥ⇵ᱶ⦹۵ႊჶುᮥᱽ⦹۵ᩑǍಽᕽᕽᬙ
ࠥ℁ࠥᩎᄥ~ᙹ➉▕ŝᩎᖙǭ᮹☁ḡᯕᬊ✚ᖒᨱෙ
℉ࢱeḲᵲශᮥᔑ⇽⦹ᩍࠥ℁ࠥ᮹ᰆ௹Õᖅၰᬕᩢĥ⫮᮹
⬉ᮉ⪵ᨱ ʑᩍ⦹Łᯱ ⦽݅.
1.2 ֜ଭ࣐ࢫࢺ࣑
ᅙᩑǍᨱᕽ۵℁ࠥᔑᨦᱶᅕᖝ░ᨱᕽᱽŖ⦹۵2011֥ᕽᬙ
ࠥ℁ࠥᩎᄥ⦹₉ᯙᬱᯱഭ(http://www.kric.or.kr/index.jsp)
ෝၵ┶ᮝಽ℉ࢱeḲᵲශ✚ᖒᮥᔑ⇽⦹Ł, ~ᙹ➉▕ŝ
☁ḡᯕᬊ✚ᖒᮥၹᩢ⦽℉ࢱeḲᵲශᔑᱶႊჶುᮥᱽ⦹Ł
ᯱ ⦽݅. ᩑǍ᮹ ⮱ᮡ Fig. 1ŝ z݅.
ࠥ℁ࠥᩎ᮹℉ࢱeḲᵲශᮡᩎᮥᯕᬊ⦹۵~ᙹ➉▕
ŝḢᱲᱢᯙšಉᯕᯩᮝ໑, ☁ḡᯕᬊ✚ᖒŝƱ☖✚ᖒᮡๅᬑၡᱲ
⦽šಉᯕᯩᮝအಽ, ᩎᖙǭ᮹✚ᖒੱ⦽⧕ݚᩎᔍෝᯕᬊ⦹۵
~ᙹ➉▕ŝʫᮡšಉᖒᮥw۵݅. ᕽ᯦ಆᄡᙹಽᕽ~
ᙹ ➉▕ ᄡᙹ᪡ ☁ḡᯕᬊ ᄡᙹෝ ᖅᱶ⦹ʑಽ ⦽݅. ~ᙹ
➉▕ᄡᙹಽ۵bࠥ℁ࠥᩎᔍᄥ⦹₉ᯙᬱ᮹✚Ḷᮥӹ┡ԝ
ᙹᯩ۵ᯝ⠪Ɂ~ᙹ, ᪅ᱥ℉ࢱe~እᮉ(2e), ᪅⬥℉ࢱ
e~እᮉ(2e), እ℉ࢱe~እᮉ(16e), ᱥℕᯙᬱᙹ
ݡእ ₉~ᙹ እᮉ ॒ 5}᮹ ᄡᙹෝ ᔍᬊ⦽݅.
☁ḡᯕᬊᄡᙹಽ۵ᕽᬙᙹ⊹ḡᱢࠥ᪡Õ⇶ྜྷݡᰆ(2009֥
ʑᵡ) ᯱഭෝ ᯕᬊ⦹ᩍ ᩎᖙǭᮥ ᖅᱶ⦹Ł ᬊࠥᄥ Location Quotient(LQ) ḡᙹෝ ᔑ⇽⦽݅(Kim, 2012). ☁ḡᯕᬊ ᬊࠥ۵
ᵝÑ, ᔢᨦ, ᨦྕ, ʑ┡॒4aḡಽǍᇥ⦽݅. LQḡᙹ۵ᱥǎ᮹
࠺ᯝᔑᨦŝእƱ⦹ᩍ✚ᱶḡᩎᔑᨦ᮹ᵲࠥෝ⊂ᱶ⦹۵ႊჶᮝ ಽᬊࠥᄥLQḡᙹෝᔑ⇽⧉ᮝಽ៉ᩎᖙǭ᮹☁ḡᯕᬊ✚ᖒᮥŁಅ
⧁ᙹᯩ݅. ݉, ᩎᖙǭ}ၽ໕ᱢᯕ0.05% ၙอᯙᩎᮡᇥᕾᨱ
ᯩᨕᯕᔢ⊹ಽeᵝ⦹ᩍᱽ⦹ᩡ݅. ᩎᖙǭᮡࠥĥ⫮ჶ᮹ḡǍ ᔢᖙĥ⫮ ḡ⋉ᨱ ᩎᮥ ʑᵡᮝಽ ၹĞ 500mಽ ᖅᱶ⦽݅.
bᩎᔍᄥᄡᙹsᮥᯕᬊ⦹ᩍᯙᇥᕾ(factor analysis)ႊჶ
ᵲᵝᖒᇥᇥᕾŝǑḲᇥᕾ(cluster analysis)ႊჶᵲĥ⊖ᱢǑḲᇥ ᕾᮥ☖⧕ᩎᔍॅᮥᮁᔍ⦽✚ᖒᮥw۵ə൚ᮝಽྗᨕbə൚᮹
✚ᖒᮥ⪶ᯙ⦽݅. ᯙᇥᕾᯙ⧪᮹⧕ᕾᮥ᳡ᬊᯕ⦹í
⦹ʑ᭥⧕ḢƱ⫭ᱥႊჶ(orthogonal rotation method) ᵲᄁญๆᜅ (varimax)ჶᮥᯕᬊ⦹ᩍ⫭ᱥ┅໑, ǑḲᇥᕾǑḲݡᔢe᮹
Ñญ۵ᮁⓕญॵᦩᱽŒÑญෝᯕᬊ⦽᪡ऽ(Ward’s linkage) ႊჶ
ᮥ ᔍᬊ⦽݅.
ǑḲᇥᕾᙹ⧪⬥ᨱ۵ᔩíÕᖅࢁᩎᯕᨕ۱ə൚ᨱ⡍⧉ࢁḡ
ෝđᱶ⦹ʑ᭥⦹ᩍə൚⧁ݚŝᱶᮥᖅᱶ⦽݅. ᯕෝ᭥⧕ə൚
eᨱ॒ᇥᔑၰ⠪Ɂ᮹࠺ᯝᖒᨱݡ⦽áᱶᮥ⦹Ł, ə൚᮹₉ᯕෝ
aᰆᖅ⦹۵ᄡᙹᨱݡ⧕ᕽbox-plotᮥ☖⧕ə൚ᯕӹڹ۵
Ğĥsᮥ ᖅᱶ⦽݅. ᇥᕾᮥ ᭥⦽ ☖ĥᱢ ⥥ಽəఉᮝಽ۵ SPSS 19.0, SAS 9.3ŝ ArcGIS 9.3ᮥ ᔍᬊ⦹ᩡ݅.
2. ʑ᳕ྙ⨭Łₑၰႊჶುá☁
2.1 షܪਏԩுணࠔւߛ֜
ࠥಽƱ☖ప᮹℉ࢱeḲᵲශᨱšಉࡽᩑǍ۵݅ᙹḥ⧪ࡹᨩ
݅. Chung et al.(2009)᮹ᩑǍᨱᕽ۵ݡᱥ, ᇡᔑ, ᬙᔑŲᩎǭᮥ
ݡᔢᮝಽeݡᄥƱ☖ప᮹⠪Ɂŝ⢽ᵡ⠙₉ෝᱢᬊ, ℉ࢱe
Ḳᵲශᮥǭᩎᄥಽᔑ⇽⦹ᩡ݅. ᇥᕾđŝ, ᇡᔑŲᩎ᮹Ğᬑ
℉ࢱ4e࠺ᦩ8.1%, ݡᱥŲᩎ۵℉ࢱ3e࠺ᦩ7.7%, ᬙᔑŲ ᩎ۵ ℉ࢱ 4e ࠺ᦩ 8.3%᮹ ℉ࢱe Ḳᵲශᮥ ӹ┡ԩ݅.
Sung et al.(2009) ᮡࠥಽƱ☖పᯱഭෝᯕᬊ⦹ᩍeݡᄥ
Ʊ☖ప᮹➉▕ᇥᕾᮥ☖⦽ᮁᱥᯱŁญ᷹ᮥᱢᬊ⦹ᩍ᪡⧕ḡ ᱱ(break point)ෝᦥ℉ࢱeݡ᪡እ℉ࢱeݡෝǍᇥ⦹ᩡ
݅. ᇥᕾđŝᬊ₉᮹Ğᬑ℉ࢱ11e࠺ᦩ℉ࢱeḲᵲශᯕ
6.58%, ✙᮹Ğᬑ℉ࢱ11e࠺ᦩ℉ࢱeḲᵲශᯕ6.65%ᯙ
äᮝಽ ᇥᕾࡹᨩ݅.
Kim and Chang(2012) ᮡeݡᄥࠥಽƱ☖పᮥ⪝⧊ǑḲᇥ ᕾᮥ☖⦹ᩍᬊ₉, ✙, ᱥ₉᳦ᨱݡ⦹ᩍ℉ࢱ, እ℉ࢱၰᝍ
eݡಽ Ǎᇥ⦹ᩡ݅. ᇥᕾđŝ ᬊ₉۵ 6.05%, ✙ᮡ 6.27%, ᱥ₉᳦ᨱ ݡ⧕ᕽ۵6.08%᮹℉ࢱe Ḳᵲශᮥ w۵ äᮝಽ
ऽ్ԍ݅.
℁ࠥ᮹℉ࢱeḲᵲශᨱš⦽ᩑǍ۵ࠥಽᨱእ⧕ᕽᇡ᳒⦽
ᝅᱶᯕ݅. Korea Rail Network Authority(2010b)ᨱᕽ۵ǎaƱ
☖DB᮹ʑ᳦ᱱ☖⧪పᯱഭෝaḡŁ℉ࢱeݡ᪡እ℉ࢱeݡ ᮹ḲᵲශᮥᄡĞ⦹໕ᕽ☖⧪eᮥᔑᱶ⦹Ł, ᯕෝᝅ⊂sŝእƱ
⦽đŝᝅᱽ☖⧪eᨱaᰆɝᱲ⦹۵℉ࢱeݡ᮹Ḳᵲශᮥ
᧞8.5%, እ℉ࢱeݡ᮹Ḳᵲශᮥ᧞5.1%ಽ⇵ᱶ⦹ᩡ݅. ⦹ḡอ
ᯕ۵ ᝅ⊂ᯱഭ᮹ ʑၹᯕ ᦥܭ ☖⧪e᮹ ᔑᱶᮥ ᭥⧕ ᯥ᮹ಽ
℉ࢱeḲᵲශᮥ᳑ᱶ⦹ᩡŁ, ℁ࠥ᮹℉ࢱeḲᵲශᮥᔑᱶ⦹
۵ߑᨱᯩᨕࠥಽƱ☖పʑၹ᮹ᯱഭෝᯕᬊ⦽݅۵⦽ĥaᯩ݅.
Kim(2007) ᮡ2003֥뺷ᕽᬙaǍ☖⧪ᝅ┽᳑ᔍ뺸Gᯱഭෝᯕᬊ
⦹ᩍ ᪅ᱥ ℉ࢱ 2e, ᪅⬥ ℉ࢱ 2e ࠺ᦩ᮹ ᬊ₉, ქᜅ,
℁ࠥᙹ݉᮹Ḳᵲශᮥᔑ⇽⦹ᩍŲᩎ☖⧪᮹✚ᖒᮥᇥᕾ⦹ᩡ݅.
۵ ݅ ᕽᬙ ԕᇡ, ᮁ⇽, ᮁ᯦ 3aḡಽ ᇥඹ⦹ᩍ ᔑ⇽⦹ᩡ݅.
℁ࠥ ᙹ݉᮹ Ğᬑ ᪅ᱥ ℉ࢱ 2e࠺ᦩ ᕽᬙ ԕᇡ᮹ Ḳᵲශᮡ
18.0% ᯙߑእ⧕ᕽᕽᬙಽᮁ᯦ࡹ۵ᯕᬊ~᮹Ḳᵲශᮡ28.9%ಽ
׳ᮡእᮉᮥӹ┡ԩ݅. ⦹ḡอᯕ۵יᖁ᮹ႊ⨆ŝḡᩎᨱ
ḲᵲශᮥǍ⧩ʑভྙᨱbᩎᯕӹᩎeǍe᮹Ḳᵲශᮥ⇵ᱶ⦹۵ ߑ ⦽ĥa ᯩ݅.
Korea Rail Network Authority(2010a) ᨱᕽ۵Ųᩎ℁ࠥ᮹30 ᇥe~Ḳᵲශᮥ3aḡಽᇥඹ⦹ᩍƱᨦྕḡǍᨱ10%, ᔢᨦ ḡǍᨱ5 ~ 9%, ⓑክঊᩑđၰ⪹Ǎᨱ15 ~ 20%ಽᱽ⦹Ł
ᯩ݅. ⦹ḡอ ᇥඹ ʑᵡᯕ ⪶⯩ ᱽࡹᨕ ᯩḡ ᦫŁ, Ḳᵲශ
ੱ⦽✚ᱶ sᯕ ᦥܭჵ᭥sᮝಽอ ᱽࡹᨕ ᩑǍᯱaə sᮥ
ᱢᬊ⦹۵ߑᨕಅᬡᯕᯩ݅. ੱ⦽Ųᩎ℁ࠥᨱݡ⧕ᕽอ℉ࢱe
Ḳᵲශᮥ݅Łᯩʑভྙᨱࠥ℁ࠥᨱᱢᬊ⦹۵ߑᨱ⦽ĥa
ᯩ݅.
ᖁ⧪ᩑǍᔍಡᨱᕽᔕ⠕ᅕᦹॐᯕ, ℁ࠥᙹ݉᮹℉ࢱeḲᵲශ ᨱš⦽ᩑǍ۵ๅᬑᇡ᳒⦽ᝅᱶᯕ݅. ✚⯩, יᖁႊ⨆ᯕӹᯝŝ
zᮡ ᯙ᭥ᱢᯙ Ǎᇥᨱ Ñᱢᯙ sᮝಽ ᱽŖࡹʑ ভྙᨱ, ᝁȽᩎᔍᖅᨱḢᱲᱢᮝಽᱢᬊ⦹ʑᨕಖ݅۵⦽ĥෝw۵݅.
ᯕᨱᅙᩑǍᨱᕽ۵bࠥ℁ࠥᩎᔍᄥ⦹₉ᯕᬊ~᮹✚ᖒŝ
ᩎᖙǭ᮹☁ḡᯕᬊ✚ᖒᮥŁಅ⦹ᩍᯙᇥᕾŝǑḲᇥᕾᮥ⪽ᬊ, bb᮹ᩎᔍᄥ✚ᖒᮥŁಅ⦽℉ࢱeḲᵲශᮥᔑ⇽⦹۵ႊჶು
ᮥᱽ⧉ᮝಽ៉ᝁȽᩎᔍᖅ᮹ᖅĥၰᬕᩢ᮹ɝÑෝษಉ⦹Ł
ᯱ ⦽݅.
2.2 ۗ࣡߆ंজଲߨࢫւߛ֜
bࠥ℁ࠥᩎ᮹☖⧪✚ᖒᮡᅖᙹ᮹ᗭॅಽᇡ░ၽᔾ⦹۵
äᯕအಽᯕෝ⧊ญᱢᮝಽᯕ⧕⦹Łᇥᕾ⦹ʑ᭥⧕ᕽ۵݅ᄡప ᇥᕾᯕ⦥⦹݅. ᅙᩑǍᨱᕽ۵ᩍ్݅ᄡపᇥᕾႊჶುᵲᩍ్
ᗭॅe᮹šĥෝ❭ᦦ⦹۵ߑᬊᯕ⦽ ᯙᇥᕾŝǑḲᇥᕾᮥ
ᱢᬊ⦹Łᯱ ⦽݅.
ᯙᇥᕾᯕᩍ్}᮹ᄡᙹॅᯕᕽಽᨕਜíᩑđࡹᨕᯩ۵
aෝᇥᕾ⦹ᩍᯕॅᄡᙹe᮹šĥෝŖ࠺ᯙᮝಽᖅ⦹۵ᇥᕾ
ʑჶᯕ݅(Hair et al., 1987). ᯕ۵ᯙᮥ⇵⇽⦹۵ŝᱶŝᯙ⧪
ᮥ⫭ᱥ⍽ᯙᇡ⦹పᮥ᳑ᱩ⦹۵ŝᱶᮝಽǍᖒࡽ݅. ᇥᕾᮥ
᭥⦽ ᯙᮥ ⇵⇽⦹۵ ႊჶᨱ Ⓧí ᵝᖒᇥᇥᕾ(principle component analysis)ŝŖ☖ᯙᇥᕾ(common factor analysis) ಽӹ٭ᙹᯩ۵ߑ, ᯕᵲᵝᖒᇥᇥᕾᮡ݅᧲⦽ᄡᙹॅ᮹šĥෝ
Łಅ⦹ᩍᯕෝaᰆฯᯕᖅ⧁ᙹᯩ۵ᗭᙹ᮹ᯙᮥ⇵⇽⦹Łᯱ
⧁ ভ ᔍᬊࡽ݅. ᯙᮥ ⇵⇽⦽ ݅ᮭ, ᯙ⧪ᮥ ⫭ᱥ⍽ b
ᄡᙹॅᯕ✚ᱶᯙᨱ۵׳ᮡᯙᇡ⦹పᮡ, ݅ෙᯙᨱ۵ԏᮡ
ᯙᇡ⦹పᮥwࠥಾ⦽݅. ᯙ⧪ᮥ⫭ᱥ┅۵ႊჶᨱ۵Ḣb
⫭ᱥႊჶŝ እḢb⫭ᱥႊჶᯕ ᯩ݅.
ǑḲᇥᕾᯕᄡᙹॅᯕw۵݅᧲⦽✚ᖒॅᮥᮁᔍᖒ(similarity)
ᮥʑᵡᮝಽ⦹ᩍእ⦽✚ᖒᮥw۵ݡᔢॅᮥ࠺ᯝ⦽Ḳ݉ᮝಽ
ྗ۵ႊჶᯕ݅(Kim and Jhun, 1990). ǑḲᇥᕾᮡǑḲ⪵ႊჶᨱ
ĥ⊖ᱢ(hierarchical) ǑḲ⪵ႊჶŝእĥ⊖ᱢ(non-hierarchial) ǑḲ⪵ ႊჶᮝಽ ӹڽ݅. ĥ⊖ᱢ ǑḲ⪵ ႊჶᮡ ᮁᔍࠥa aᰆ
ⓑݡᔢॅᮥ₉ಡಽྗᨕӹaÑӹᮁᔍࠥaaᰆ᯲ᮡݡᔢॅᮥ
Table 1. Variables Setting
Classification Name of variable (abbreviation) Calculating method
Passenger demand pattern
Average daily number of passengers (AD) ć
ÐÓÒ
ƍƒſƊƎſƑƑƃƌƅƃƐ Percentage for the number of passengers
in the morning peak hours (MP) ć ©ſƑƑƃƌƅƃƐƑƇƌƋƍƐƇƌƅƎƃſƉƆƍƓƐƑÝ Î×× ƍƒſƊƎſƑƑƃƌƅƃƐ Percentage for the number of passengers
in the afternoon peak hours (AP) ©ſƑƑƃƌƅƃƐƑƇƌſƄƒƃƐƌƍƍƌƎƃſƉƆƍƓƐƑÝ Î×× ƍƒſƊƎſƑƑƃƌƅƃƐ Percentage for the number of passengers
in the non-peak hours (NP) ć ©ſƑƑƃƌƅƃƐƑƇƌƌƍƌà ƎƃſƉƆƍƓƐƑÝ Î×× ƍƒſƊƎſƑƑƃƌƅƃƐ Percentage for the number
of boarding passengers (NBP) ć ƍſƐƂƇƌƅƎſƑƑƃƌƅƃƐ Ý Î×× ƍƒſƊƎſƑƑƃƌƅƃƐ
Land use inventory
Residential LQ Index (R-LQ)
ć ƆƃſƐƃſƍƄƃſƁƆƓƑƃƇƌ¬ƃƍƓƊ ƆƃƅƐƍƑƑſƐƃſƇƌ¬ƃƍƓƊ
ƆƃƅƐƍƑƑſƐƃſƇƌƑƒſƒƇƍƌƇƌƄƊƓƃƌƁƃſƐƃſ
ƆƃſƐƃſƍƄƃſƁƆƓƑƃƇƌƑƒſƒƇƍƌƇƌƄƊƓƃƌƁƃſƐƃſ Commercial LQ Index (C-LQ)
Business LQ Index (B-LQ)
₉ಡಽᱽÑ⧕ӹa۵ႊᮝಽǑḲᯕ⩶ᖒࡹ۵ŝᱶᮥ❭ᦦ⦹ʑ
ᬊᯕ⦹ḡอ, ᯱഭ᮹ᙹaḡӹ⊹íฯᮝ໕ᇥᕾ⦹ʑᨕಅᬕ݉ᱱᯕ
ᯩ݅.
ᯕᨱእ⧕እĥ⊖ᱢǑḲ⪵ႊჶᮡǑḲ᮹}ᙹෝၙญᱶ⦹Ł
ǑḲ᮹ᵲᝍᨱaᰆᮁᔍ⦽✚ᖒᮥw۵ݡᔢᮥ₉ಡಽ⡍⧉⧕ӹa ۵ႊᮝಽᯱഭ᮹ᙹaฯᮥĞᬑŁᛞíᇥඹ⧁ᙹᯩᮝӹ
ǑḲ⩶ᖒⅩʑsᨱđŝaᔢᯕ⦽݉ᱱᯕᯩ݅. ĥ⊖ᱢ
ǑḲ⪵ႊჶᨱ۵ݡᔢe᮹Ñญෝᔑᱶ⦹۵ʑᵡᨱ݉ᯝᩑđ ჶ, ⠪Ɂᩑđჶ, ᵲᝍᩑđჶ, ີॵᦩᩑđჶ, ᪥ᱥᩑđჶ, ᪡ऽႊჶ
॒ᯕ ᯩ݅.
ᯕ᪡ zᮡ ᯙᇥᕾ ၰ ǑḲᇥᕾᮡ ࠥĥ⫮ ᇥᨱᕽ ฯᯕ
ಽ18}ᄡᙹෝᔍᬊ⦹ᩍ4}᮹ᯙᮥ⇵⇽, ǑḲᇥᕾᮥ☖⧕
6}᮹ǑḲᮝಽᱥℕḡᩎᮥᇥඹ⦹ᩡ݅. Song and Chang(2010)
ᮡᔍ⫭ྙ⪵ᱢᗭၰྜྷญᱢᗭ᪡šಉࡽ10}᮹ᄡᙹෝ☖⧕
ᯙᇥᕾᮝಽ 4}᮹ ᯙᮥ ⇵⇽⦹ᩍ ǑḲᇥᕾᮥ ☖⧕ ᙹࠥǭ
ࠥॅᮥ5}᮹ǑḲᮝಽᇥඹ⦹ᩡ݅. Lee et al.(2012)ᮡ9}᮹
ᔢᨦḡᩎ᮹ትಾᄥᔢᨦ⪵ᮉၰ⠪Ɂᩑ໕ᱢ॒9}᮹ᄡᙹॅᮥ
ࠥ⇽⦹ᩍᯙᇥᕾᮥ☖⦽2aḡᯙᮥ⇵⇽, ᯕෝ݅ǑḲᇥᕾ
ᮥ ☖⧕ Ğʑࠥ ᔢᨦḡᩎᮥ 5aḡ ᮁ⩶ᮝಽ ᇥඹ⦹ᩡ݅.
Choi et al.(2007) ᮡŁᗮࠥಽ᮹ᮁ⩶ᄥಽᖅĥᗭa₉ᄥࡹᨕ
⦽݅۵ᱱᨱɝÑ⦹ᩍŁᗮࠥಽeƱ☖ప᮹AADT, ᄡ࠺ĥᙹ,
᪽ࠥĥᙹ॒᮹7aḡᄡᙹෝ☖⦹ᩍᯙᇥᕾᮥ☖⧕2aḡᯙᮥ
⇵⇽, ᱥǎ᮹Łᗮࠥಽෝ5aḡᮁ⩶ᮝಽᇥඹ⦹ᩡ݅. ᯕ᪡zᯕ
ᯙᇥᕾŝǑḲᇥᕾᮥᙽ₉ᱢᮝಽᙹ⧪⦹ᩍᱥℕḡᩎᮥ✚ᱶᮁ
⩶ᨱaḡǑḲॅಽᇥඹ⧁ᙹᯩ݅. ᯕ᪡zᮡᩑǍॅᨱᕽ ۵, ᯝၹᱢᮝಽᄡᙹᖁᱶ᮹᪅ඹෝ↽ᗭ⪵⦹ʑ᭥⧕ᕽᯙᇥᕾᮥ
☖⦹ᩍ ᄡᙹॅ e᮹ ᵲᅖᇡᇥᮥ ᱽÑ⦽ ᯙᱱᙹෝ ࠥ⇽⦽ अ, ᯕෝ ǑḲᇥᕾ᮹ ᖅᄡᙹಽ ᔍᬊ⦹ᩡ݅.
3. ᇥᕾᯱഭ
bᩎᔍᄥ✚ᖒᮥӹ┡ԕʑ᭥⧕5aḡ~ᙹ➉▕šಉᄡᙹ
᪡4aḡ☁ḡᯕᬊšಉᄡᙹෝᔑ⇽⦹ᩡ݅. ~ᙹ➉▕šಉᄡ ᙹ۵⩥ᰍbᩎᔍෝᯕᬊ⦹۵~ᙹ➉▕᮹✚ᖒᮥӹ┡ԕʑ
᭥⧕ᔑ⇽⦹ᩡŁ, ☁ḡᯕᬊᄡᙹ۵ə൚ᄥᇥඹෝᰆ௹ᨱᱢᬊ⦹ʑ
᭥⧕LQḡᙹಽᙹ⊹⪵⦹ᩍᔑ⇽⦹ᩡ݅. ᄡᙹᨱš⦽ᖅᮡTable 1 ᨱᱽࡹᨕᯩ݅. ᔑ⇽ࡽ9aḡᄡᙹෝ☖⧕ᯙᇥᕾᵲᵝᖒᇥᇥ ᕾᮥᝅ⦹ᩍᵝᯙॅᮥᎲᦥԕŁ, ᯕෝ☖⧕ᇡᩍࡽᯙᇡ⦹
పᮥaḡŁĥ⊖ᱢǑḲᇥᕾᮥᝅ⦹ᩍᮁᔍ⦽✚ᖒᮥӹ┡ԕ۵
ᩎॅᮥə൚ᮝಽ ྗᮡ ݅ᮭb ə൚ᨱ ⧕ݚ⦹۵ᩎᮥ ᯕᬊ⦹۵
ᯙᬱᙹෝ eݡᄥಽ ⧊ᔑ⦹ᩍ ℉ࢱe Ḳᵲශᮥ ᔑᱶ⦽݅.
5aḡ᮹~ᙹ➉▕šಉᄡᙹᵲ, ᯝ⠪Ɂ~ᙹ۵ᩎᔍᄥ ಽᔍ௭ॅᯕฯᯕᯕᬊ⦹۵ᩎŝᱢíᯕᬊ⦹۵ᩎ᮹₉ᯕෝӹ┡
ԕʑ᭥⦹ᩍᔑ⇽⦹ᩡᮝ໑, ᪅ᱥ℉ࢱeݡ(07:00~09:00), ᪅⬥
℉ࢱeݡ(18:00~20:00) ၰእ℉ࢱeݡ(05:00~07:00, 09:00
~18:00, 20:00~01:00)ෝᖅᱶ⦹ᩍbeݡᄥᨕ۱ᱶࠥእᮉಽ
ᔍ௭ॅᯕᯕᬊ⦹۵ḡᦥᅕʑ᭥⧕ᔑ⇽⦹ᩡ݅. ₉~ᙹእᮉ
ᮡbᩎᔍෝᯕᬊ⦹۵⦹₉ᯙᬱᵲᨱᕽ₉⦹۵ᯙᬱ᮹እᮉಽ
ᯕᬊ~ᵲ₉ᯙᬱŝ⦹₉ᯙᬱ᮹✚Ḷᮥᦥᅕʑ᭥⦹ᩍᱢᬊ⦹
ᩡ݅. ᩎᖙǭ᮹☁ḡᯕᬊŝšಉࡽ4aḡᄡᙹ۵ᵝÑ, ᔢᨦ, ᨦྕ, ʑ┡ᬊࠥᄥLQḡᙹಽᕽbᩎᖙǭ᮹☁ḡᯕᬊ✚ᖒᮥ❭ᦦ⦹ʑ
᭥⧕ ᔑ⇽⦹ᩡ݅.
Table 2. Factor Scores of Three Factors
Variables Factor 1 Factor 2 Factor 3
R-LQ 0.729 0.164 -0.067
MP 0.709 -0.023 0.423
NBP 0.514 0.362 0.231
C-LQ 0.114 0.630 0.372
Rs-LQ 0.303 0.527 0.320
NP 0.078 0.631 0.203
B-LQ -0.163 0.237 0.814
AP 0,205 0.241 0.632
AD -0.062 0.353 0.868
Individual amount
for explanation (%) 44.83 24.61 11.16 Accumulation
amount for explanation (%)
44.83 69.44 80.60
4. ႊჶುǍ⇶
4.1 ૬ंজ
ⅾ213}᮹ᩎᨱݡ⦹ᩍᄡᙹෝᖅᱶ, ᯕᵲ200}ᩎᮥݡᔢᮝಽ
ᇥᕾᮥᝅ⦹ᩡŁ, 13}᮹ᩎᮡđŝá᷾ᨱᱢᬊ⦹ᩡ݅. 13}᮹
ᩎᮡ݉ᙽᯥ᮹⢽ᅙ⇵⇽(simple random sampling) ႊჶᮥ☖⧕
⇵⇽⦹ᩡ݅. 200}ᩎ᮹ᄡᙹsᨱݡ⦽ᵝᖒᇥᇥᕾᮥᝅ⦹ᩡ݅.
ᦿᕽᨙɪ⦽ၵ᪡zᯕ, ᯙ⧪᮹⧕ᕾᮥ᳡ᬊᯕ⦹í⦹ʑ
᭥⧕ḢƱ⫭ᱥႊჶᵲᄁญๆᜅჶᮥᯕᬊ⦹ᩍ⫭ᱥ⎑݅. ᯙᇥ ᕾ᮹┡ݚᖒᮥ⪶ᯙ⦹ʑ᭥⧕Kaiser-Meyer-Oklin(KMO) áᱶŝ
Bartlett ᮹Ǎ⩶ᖒáᱶᮥᝅ⦹ᩡ݅. áᱶđŝKMO sᯕ0.613 ᮝಽᇥᕾᨱᱢ⧊⦽äᮝಽ❱ࡹᨩŁ, Bartlett᮹Ǎ⩶ᖒáᱶ
đŝᩎᮁ᮹⪶ශᯕ0.000ᮝಽᄡᙹॅᯕᇥᕾᨱᱢᱩ⦽äᮝಽ
ӹ┡ԍ݅.
ᱢᱶᯙ᮹ᙹෝđᱶ⦹۵ߑᯩᨕᜅⓍญࠥ⢽ෝᯕᬊ⦹ᩡ۵ߑ,
ᯙ᮹Łᮁsᯕ1.0ᯕᔢᯕࡹ۵ᯙॅᯕ⇵⇽ᨱᱢᱩ⦽⬥ᅕa
ࢁᙹᯩ݅. bᯙॅᮡᄡᙹᨱݡ⧕ᇡ⦹ప(factor score)ᮥaḡ໑, ᯕ۵ᨕਅᯙॅᯕᨕਅᄡᙹॅŝaᰆฯᮡšĥෝaḡŁᯩ۵ḡ
ᙹ ᯩ۵ ĥᙹಽᕽ, ĥᙹs ᱽŒ᮹ ႒ᇥᮉᮡ ə ᯙᨱ ᮹⧕
ᖅࡹ۵ ᄡᙹ᮹ ᇥᔑ᮹ እᮉᮥ ӹ┡ԙ݅.
ᯙᇥᕾđŝFig. 2᪡zᯕᯙ᮹Łᮁsᯕ1.0 ᯕᔢᯕࡹ۵
3}᮹ᯙᯱॅᯕ⇵⇽ࡹᨩŁᱥℕᯱഭᨱݡ⦹ᩍ80.60%᮹ᖅಆ
ᮥw۵äᮝಽӹ┡ԍ݅. ᱽ1ᯙᯱ۵ᱥℕᄡ࠺᮹44.83%ෝᖅ⦹
໑, ᵝÑLQḡᙹ, ᪅ᱥ℉ࢱe~እᮉ, ₉~ᙹእᮉŝᩑš ᖒᯕ׳ᮡäᮝಽᇥᕾࡹᨩ݅. ᯕ۵ᵝÑḡᩎᨱ᭥⊹⦽ࠥ℁ࠥᩎ ᯕ᪅ᱥ℉ࢱeݡᨱ☖ɝၰ☖⦺☖⧪ᮝಽᯙ⦹ᩍ׳ᮡ℉ࢱe
Ḳᵲශᮥӹ┡ԙ݅۵ᱱᮥŁಅ⧁ভ, ᱽ1ᯙᯱ۵ᵝÑ✚ᖒᯕv⦽
ḡᩎᨱ ᭥⊹⦽ ࠥ℁ࠥᩎॅ᮹ ᗮᖒᮝಽ ᅝ ᙹ ᯩ݅.
ᱽ2ᯙᯱ۵ᱥℕᄡ࠺᮹24.61%ෝᖅ⦹໑, ᔢᨦLQḡᙹ, ʑ┡
LQḡᙹၰእ℉ࢱe~እᮉᨱᕽ׳ᮡ᧲᮹ᇡ⦹పᮥӹ┡ԙ݅.
እ℉ࢱeݡ᮹ࠥ℁ࠥᩎᯕᬊእᮉᮡᔢᨦḡᩎŝʑ┡ḡᩎ(Ŗ Ŗᖅ, šŲ⮕íᖅ ၰ ᯱ࠺₉ šಉᖅ ⡍⧉ḡᩎ)ᨱᕽ ׳ᮡ
sᮥ aḥ݅. ᯕౝ ᱽ2ᯙᯱ۵ ʑ┡ḡᩎᮥ ⡍⧉⦽ ᔢᨦ✚ᖒᯕ
v⦽ ḡᩎᨱ ᭥⊹⦽ ࠥ℁ࠥᩎॅ᮹ ᗮᖒᮝಽ ❱݉ࡽ݅.
ᱽ3ᯙᯱ۵ᱥℕᄡ࠺᮹11.16%ෝᖅ⦹໑, ᨦྕLQḡᙹၰ
᪅⬥℉ࢱe~እᮉŝᯝ⠪Ɂ~ᙹ᪡׳ᮡᩑšᖒᮥaḡ۵
äᮝಽ ӹ┡ԍ݅. ᨦྕḡᩎᨱ ᭥⊹⦽ ࠥ℁ࠥᩎᮡ ᯝၹᱢᮝಽ
᪅⬥℉ࢱeݡᨱ♕ɝ⦹۵~ᙹእᮉᯕ׳ʑভྙᨱ, ᱽ3ᯙᯱ
۵ᨦྕ✚ᖒᯕv⦽ḡᩎᨱ᭥⊹⦽ࠥ℁ࠥᩎॅ᮹ᗮᖒᮝಽŁಅ
ࡽ݅. ᯱᖙ⦽ᄡᙹᄥbᯙᯱᨱݡ⦽ᇡ⦹పᮡTable 2᪡z݅.
4.2 ֞ுंজէր
3 }᮹ᯙᯱᨱݡ⦽bᩎॅᯕaḡ۵ᯙᯱाᱱᮥᯕᬊ⦹ᩍ᪡ऽ ᮹ႊჶᮥ☖⦽ĥ⊖ᱢǑḲᇥᕾᮥᝅ⦹ᩡ݅. ᇥᕾđŝᔑ⇽ࡽ
▱ऽಽəఉᮥၵ┶ᮝಽə൚}ᙹ᮹ᄡ⪵ᨱෙPseudo-F ☖ĥప ᮹ᄡ⪵ෝšₑ⦹ᩡ݅. ᯕ۵ə൚e᮹ᇥญᱶࠥෝ⊂ᱶ⦹۵ႊჶᮝ ಽ, ǑḲᇥᕾᨱᕽ۵ə൚}ᙹ᮹᷾a⧁ভPseudo-F ☖ĥపᯕ
ɪĊ⦹í ⍅ḡ۵ ḡᱱᨱ ݡ᮲ࡹ۵ ə൚᮹ }ᙹෝ ᱢᱶ ə൚᮹
ᙹಽ ᱶ⧁ ᙹ ᯩ݅.
እƱđŝə൚᮹}ᙹa3}ᯝভPseudo-F ☖ĥపᯕaᰆ
ɪĊ⦹í᷾a⦹အಽə൚᮹}ᙹ۵3}aᱢᱩ⦽äᮝಽ❱݉⦹ᩡ
݅. ǑḲᇥᕾ đŝ b ə൚ᄥ ⧕ݚ ᩎᮡ Table 3ŝ z݅.
ə൚1ᨱᗮ⦽ᩎॅᮡ94}ಽaᰆฯŁ, ⠪Ɂᱢᮝಽᱽ1ᯙᯱᨱ
ݡ⦽ᯙᯱाᱱᯕ݅ෙə൚ᨱእ⧕׳ᮡ᧲᮹sᮥӹ┡ԕŁᯩᨕ
Table 3. Station Names of Each Group Groups
(number) Stations
Group1 (94)
Gangnam-Gu Office, Gangdong, Gang-dong-Gu Office, Gaerong, Gaehwasan, Geoyeo, Gongdeok, Gongneung, Gwang-naru, Gwangheungchang, Gusan, Gu-ui, Gupabal, Gireum, Kkachisan, Nakseongdae, Namguro, Namssong, Namtaeryeong, Nokbeon, Noksapyeong, Dapsimni, Danggogae, Daheung, Dogok, Dorimcheon, Dobongsan, Dokbawi, Dunchon-dong, Digital Media City, Ttukseom, Madeul, Majang, Macheon, Mapo-Gu Office, Mangwon, Maebong, Meokgol, Myeongil, Mongchontoseong, Mullae, Bangbae, Bangi, Banghwa, Beotigogae, Boramae, Bomun, Bokjeong, Bongcheon, Bonghwasan, Sangdo, Sangwangsimni, Sangwolgok, Seodaemun, Seokgae, Seokchon, Seongsu, Singil, Sindaebang, Sindaebang samgeori, Sinjeong, Sincheon, Sinpung, Amsa, Aeogae, Yangpyeong, Yeokchon, Yeongdeungpo-gu Office, Ogeum, Omokgyo, Onsu, Yongmasan, Ujangsan, Wolgok, Ilwon, Jamsilnaru, Jamwon, Jangseungbagi, Junggye, Junghwa, Jeungsan, Changsin, Chunwang, Chunggu, Chungdam, Taereung, Hakdong, Hangdang, Hwagok, Hwarangdae, Hyochang park
Group2 (73)
Konkuk Univ,, Godeok, Korea Univ., Express Bus Terminal, Seoul National Univ, of Education, Gunja, Gubeundari, Geumho, Gil-dong, Naebang, Nowon, Daerim, Dongnimmun, Dongdaemun history and culture park, Dongguk Univ., Dongmyo, Dongjak, Daechi, Ttukseom Resort, Mok-dong, Mia, Balsan, Bulkwang, Sagajeong, Samgakji, Sanggye, Sangbong, Sangsu, Saejeol, Seoul National Univ., Sungsin Univ., Songjeong, Suraksan, Suyu, Sukmyong Univ., Sungsil Univ., Singeumho, Sindang, Sinyongsan, Singjeongmegeori, Sinchon, Ssangmun, Achasan, Ahyun, Anam, Apgujeong, Yaksu, Yangcheon-gu Office, Children‘s Grand Park,Yeongdeungpo Market, Oksu, Olympic Park, Wangsimni, Yongdu, Worldcup Stadium, Eungam, Itaewon, Jamsil, Jangji, Janghanpyung, Sports Complex, Chang-dong, Chunho, Hagye, Hakyeoul, Hangangjin, Hansung Univ., Hanyang Univ., Hyehwa, Hongik Univ., Honjae
Group3 (33)
Gasan Digital Complex, Gangnam, Gyeongbokgung, Gwanghwamun, Guro Digital Complex, Nambu Bus Terminal, Nonhyeon, Dangsan, Mapo, Myeong-dong, Sadang, Seocho, Sunneung, City Hall, Singdorim, Sinsul- dong, Anguk, Yangjae, Yeouinaru, Yeouido, Yeoksam, Euljiro 3-ga, Euljiro 4-ga, Euljiro 1-ga, Isu, Jonggak, Jongno 3-ga, Jongno 5-ga, Chungmuro, Chungjeongro,
Table 4. Average Factor Scores of Each Group
Groups Factor 1 Factor 2 Factor 3
Group 1 1.34 -0.72 -1.56
Group 2 -0.93 0.81 1.15
Group 3 -1.76 0.26 1.90
Table 5. Land Use Inventory of Each Group
Groups R-LQ C-LQ B-LQ Rs-LQ
Group 1 1.05 0.90 0.53 0.82
Group 2 0.68 1.52 1.34 1.26
Group 3 0.37 1.28 2.48 1.05
Average 0.80 1.19 1.15 1.02
Table 6. Demand Pattern of Each Group
Groups MP NP AP NBP AD
(/10
3) Group 1 19.84 64.43 15.70 51.29 25.40 Group 2 13.82 69.93 16.20 50.08 57.16 Group 3 17.54 65.41 17.17 49.31 96.95 ᵝÑ✚ᖒᯕ v⦽ ḡᩎᨱ ᗮ⦽ ᩎॅᯕŁ ⧁ ᙹ ᯩ݅. ə൚2ᨱ
ᗮ⦽ᩎॅᮡ73}ಽᱽ2ᯙᯱᨱݡ⦹ᩍ׳ᮡᯙᯱाᱱᮥaḡ۵
äᮝಽӹ┡ԍŁ, ᩎᔍ᮹᭥⊹ෝŁಅ⧁ভᔢᨦ✚ᖒᯕv⦽ḡᩎᨱ
ᗮ⦽ᩎॅᯙäᮝಽᇥඹ⧁ᙹᯩ݅. ə൚3ᨱ۵33}᮹ᩎᯕᗮ⧕
ᯩᮝ໑, ݅ෙə൚ᨱእ⧕ᱽ3ᯙᯱᨱݡ⦹ᩍ׳ᮡᯙᯱाᱱᮥaḥ
݅. ᩎᔍॅᯕ ੱ⦽ ݡᇡᇥᯕ ࠥᝍḡᩎᨱ ᭥⊹⦹۵ ᱱᮥ Łಅ⧁
ভᨦྕḡᩎ✚ᖒᯕv⦽ḡᩎᨱᗮ⦽ᩎॅᯙäᮝಽ❱݉ࡽ݅.
Table 4۵ Ǎℕᱢᯙ ə൚ᄥ ⠪Ɂ ᯙᯱाᱱᮥ ӹ┡ԙ݅.
ǑḲᇥᕾ đŝ ᇥඹࡽ ə൚ ᄥಽ 9}᮹ ᄡᙹᨱ ݡ⦽ ✚ᖒᮥ
ᔕ⠕ᅕᦹ݅. ຝᱡ☁ḡᯕᬊ✚ᖒ᮹Ğᬑ(Table 5), ə൚1ᨱᗮ⦽
ᩎॅᮡ݅ෙə൚ᨱᗮ⦽ᩎॅᨱእ⧕ᔢݡᱢᮝಽ׳ᮡᵝÑLQḡᙹ
sᮥw۵äᮝಽӹ┡ԍ݅. ə൚2ᨱᗮ⦽ᩎॅ᮹Ğᬑ, ݅ෙə൚ᨱ
ᗮ⦽ᩎॅᨱእ⧕ᔢᨦLQḡᙹ᪡ʑ┡LQḡᙹa׳ᮡäᮝಽӹ┡
ԍŁ, ə൚3ᨱᗮ⦽ᩎॅᮡᨦྕLQḡᙹa׳ᮡäᮝಽӹ┡ԍ݅.
ᕽᦿᕽั⦽ə൚1ᮡᵝÑ✚ᖒᯕv⦽ḡᩎᨱ᭥⊹⦽ᩎॅᯕ Ł, ə൚2۵ᔢᨦ✚ᖒᯕv⦽ḡᩎᨱ᭥⊹⦽ᩎॅᯕ໑, ə൚3ᮡ
ᨦྕ✚ᖒᯕv⦽ḡᩎᨱᗮ⦽ᩎॅᯕ۵đುᮡ┡ݚ⦽äᮝಽ
❱݉ࡽ݅.
݅ᮭᮝಽ~ᙹ➉▕✚ᖒᨱݡ⦹ᩍᔕ⠕ᅕ໕, ə൚1ᨱᗮ⦽
ᩎॅᮡ᪅ᱥ℉ࢱeݡ᮹እᮉŝ₉~ᙹእᮉᨱᕽ݅ෙə൚ ᨱእ⧕׳ᮡsᮥӹ┡ԩŁ, እ℉ࢱeݡእᮉŝᯝ⠪Ɂ~ᙹᨱ ᕽԏᮡsᮥӹ┡ԩ݅(Table 6). ə൚2ᨱᗮ⦽ᩎॅᮡእ℉ࢱe ݡእᮉᨱᕽ׳ᮡsᮥaḡ໑, ᪅ᱥŝ᪅⬥℉ࢱeݡእᮉᨱᕽ
aᰆԏᮡsᮥӹ┡ԩ݅. ə൚3ᮡ᪅⬥℉ࢱeݡእᮉŝᯝ⠪Ɂ
~ᙹᨱᕽ ׳ᮡ sᮥ ӹ┡ԩ݅.
Table 7. Peak-hour Ratio of Each Group
Groups Peak hour ratio (%)
Group 1 12.10
Group 2 8.45
Group 3 10.76
Average 10.48
Table 8. Homogeneity Test of Each Variable (Group1 vs. Others)
Variable
Levene’s test
(variance) t-test (mean)
F-value
t-value equality of variances
O
equality of variances X
R-LQ 11.454 7.611 7.787
C-LQ 12.221 -5.918 -6.070
B-LQ 54.937 -6.386 -6.650
Rs-LQ 0.790 -1.215 -1.184
AD 96.512 -8.058 -8.477
Table 9. Homogeneity Test of Each Variable (Group2 vs. Group3)
Variable
Levene’s test
(variance) t-test (mean)
F-value
t-value equality of variances
O
equality of variances X
R-LQ 3.136 4.868 5.314
C-LQ 3.869 0.565 0.635
B-LQ 15.554 -10.092 -8.495
Rs-LQ 0.268 0.568 0.626
AD 2.204 -2.499 -2.443
Fig. 3. Box Plot on Average Number of Passengers (Group1 vs.
Others)
4.3 షܪਏԩுணࠔॺ୨
↽᳦ᱢᮝಽbə൚ᄥᩎॅ᮹ᯱഭෝ⧊ᔑ⦹ᩍ℉ࢱ1e᮹
እᮉᮥ ᔑ⇽⦹ᩡ݅. ə đŝ ℉ࢱeᮡ ə൚1ŝ ə൚3᮹ Ğᬑ
᪅ᱥ8ᨱᕽ9ʭḡಽӹ┡ԍᮝ໑, ə൚1ᨱᕽ۵12.10%, ə൚3 ᨱᕽ۵10.76%᮹℉ࢱeḲᵲශᮥw۵äᮝಽӹ┡ԍŁ, ə൚2 ᨱᕽ۵᪅⬥6ᨱᕽ7ʭḡ8.45%᮹℉ࢱeḲᵲශᮥӹ┡ԕ ᨕᵝÑḡᩎᨱ᭥⊹⦽ᩎ᮹℉ࢱeḲᵲශᯕᔢᨦḡᩎᨱ᭥⊹⦽
ᩎ᮹əäᨱእ⧕᧞4%ᱶࠥ׳ᮡäᮝಽᇥᕾࡹᨩ݅(Table 7).
4.4 ֻࠄ්ۥࢺ࣑ߨ
ᔩಽᬕᩎᔍෝᖅĥ⧁ভ, ᨕਅ℉ࢱeḲᵲශsᮥᔍᬊ⧁
äᯙḡෝᱶ⦹ʑ᭥⦹ᩍᨕਅŝᱶᮥÑℱᨕ۱ə൚ᨱ⧁ݚ⧁ḡ
đᱶ⦹۵ŝᱶᯕ⦥⦹݅. ᩑǍđŝᔢݡᱢᮝಽӹນḡə൚ॅᨱ
እ⧕ᔢᯕ⦽✚Ḷᮥӹ┡ԙə൚1ᮥᬑᖁ⇵⇽⦹Ł, ᯕᨕᕽə൚2᪡
ə൚3ᮝಽᇥඹ⦹۵Ǎ᳑ෝᱢᬊ⦹ᩡ݅. ᅙᩑǍᨱᕽᔍᬊࡽ9aḡ
ᄡᙹᵲᨱᕽ, ᯝ⠪Ɂ~ᙹ۵ᙹᩩ⊂ᮥ☖⧕ᩩ⊂a܆⦹Ł4a ḡ☁ḡᯕᬊᄡᙹ۵ʑ᳕᮹☁ḡᯕᬊĥ⫮ᨱᔑ⇽ᯕa܆⦹݅.
ə൚⧁ݚႊჶು᮹ᱢᬊᮥᬊᯕ⦹íอॅʑ᭥⦹ᩍ3aḡǑḲᮝಽ
ᇥඹࡹ۵ŝᱶᨱᕽbb᮹ə൚᮹ᇥඹෝaᰆӹ┡ԝᙹᯩ۵
ᄡᙹෝ⦹ӹᦊᖅᱶ⦹ᩡ݅. ຝᱡbə൚᮹ᇥඹŝᱶᨱᕽ5aḡ
ᄡᙹᨱ ෙᇥඹ݉ĥ᮹ ࢱ ə൚ᨱݡ⦽ ⠪Ɂŝ ᇥᔑ᮹࠺ᯝᖒ
áᱶᮥᝅ⦹ᩍ, ᇥඹŝᱶݡ⢽ᖅᄡᙹෝ⇵⇽⦹ᩡ݅. áᱶ᮹
đŝಽӹ᪅۵F-value᪡t-value sᯕⓕᙹಾԏᮡᮁ᮹ᙹᵡᨱ ᕽࠥȡྕaᖅᮥʑb⧁ᙹᯩᮝအಽᇥඹŝᱶᮥӹ┡ԕ۵
ᄡᙹ ⧁ ᙹ ᯩ݅.
ᕽTable 8ŝzᯕ, ə൚1ŝʑ┡ə൚ॅಽӹڹ۵ℌჩṙ
ᇥඹ݉ĥᨱᕽ۵F-value᪡t-valuesᯕaᰆⓑ‘ᯝ⠪Ɂ~ᙹ’
ᄡᙹaə൚1᮹ᇥඹෝaᰆᖅ⦹۵äᮝಽ❱݉⦹ᩡ݅. ੱ⦽
Table 9 ᨱᕽᅕॐᯕə൚2᪡ə൚3ᮝಽӹڹ۵ࢱჩṙᇥඹ݉ĥᨱ ᕽ۵ᩎF-value᪡t-value sᯕaᰆⓑ‘ᨦྕLQḡᙹ’ ᄡᙹa
ᇥඹŝᱶᮥ aᰆ ᖅ⦹۵ äᮝಽ ❱݉⦹ᩡ݅.
ə൚⧁ݚŝᱶᨱᕽ᮹Ğĥsᮥᖅᱶ⦹ʑ᭥⦹ᩍBox Plotᮥ
ᯕᬊ⦹ᩡ݅. Fig. 3ŝFig. 4᮹ᔪᯕ⋁⧕ḥօ༉ᔢᯱᦩᨱ۵ᱥℕ
Fig. 5. Group Allocation Process
Table 10. Comparison of the Number of Passengers during One Peak-hour
Station (Group)
Experimental value
Standard guideline
(Error)
This research (Error) Gangbyeon
(2) 11,326 11,862
(+4.73%)
11,471 (+1.28%) Daecheong
(1) 3,613 2,529
(-30.00%)
3,505 (-2.99%) Myonmok
(1) 3,690 2,791
(-24.36%)
3,868 (+4.82%) Munjeong
(1) 1,341 947
(-29.38%)
1,312 (-2.16%) Mia-samgeori
(2) 8,309 7,799
(-6.14%)
7,541 (-9.24%) Banpo
(1) 1,360 1,129
(-16.99%)
1,566 (+15.15%) Samseong
(3) 19,554 15,871
(-18.84%)
19,520 (-0.17%) Songpa
(1) 1,711 1,245
(-27.24%)
1,726 (+0.88%) Suseo
(1) 3,475 2,495
(-28.20%)
3,458 (-0.49%) Ehwa Womans
Univ.
(2)
5,532 5,686
(+2.78%)
5,498 (-0.61%) Jegi-dong
(2) 5,014 5,156
(+2.83%)
4,986 (-0.56%) Junggok
(1) 2,650 1,860
(-29.81%)
2,578 (-2.72%) Cheong-ryangni
(2) 7,564 8,644
(+14.28%)
8,359 (+10.51%)
Total 75,139 68,014
(-9.48%)
75,388 (+0.33%)
ᯱഭ᮹1ᔍᇥ᭥ᨱᕽ3ᔍᇥ᭥ʭḡᯱഭ᮹ᵲᦺ50%aᗮ⦹۵ᩢᩎ ᯕ݅. ᅙᩑǍᨱᕽ۵ə൚ᇥඹ᮹ʑᵡsᮝಽ, ࢱə൚ᵲᔢݡᱢᮝ ಽԏᮡᄡᙹsᮥw۵ə൚᮹3ᔍᇥ᭥sŝ׳ᮡᄡᙹsᮥw۵
ə൚᮹ 1ᔍᇥ᭥s᮹ ᵲesᮥ ᖅᱶ⦹ᩡ݅.
Fig. 3 ᨱ໕, ᔢᯱᦩᨱᗮ⦹۵ə൚ᄥᯝ⠪Ɂ~ᙹჵ᭥۵
ə൚1᮹Ğᬑ᧞15,000ᨱᕽ32,000ᱶࠥᯕŁʑ┡ə൚(ə൚2 + ə൚3)᮹Ğᬑ᧞40,000ᨱᕽ110,000ᱶࠥᯕ݅. ᕽ
ℌჩṙ݉ĥᨱᕽᯝ⠪Ɂ~ᙹᨱෙᇥඹ᮹Ğĥsᮥࢱə൚
Ğĥ᮹ ⠪Ɂsᯙ 36,000ᮝಽ ⦹ᩡ݅.
ə൚2᪡ə൚3ᮥǍᇥ⦽Fig. 4ᨱᕽᔢᯱᦩᨱᗮ⦹۵ə൚ᄥ
ᨦྕLQḡᙹჵ᭥۵ə൚2᮹Ğᬑ᧞0.4ᨱᕽ1.3ᯕŁə൚3᮹Ğᬑ
᧞2.2ᨱᕽ4.0ᯕ݅. ə్အಽᨦྕLQḡᙹᨱෙᇥඹ᮹Ğĥsᮡ
1.75 ಽ ᖅᱶ⦹ᩡ݅. ↽᳦ ə൚⧁ݚ ŝᱶᮡ Fig. 5᪡ z݅.
5. ႊჶು᮹á᷾
ᔩಽᬕࠥ℁ࠥᩎᔍÕᖅ℉ࢱeḲᵲශᮡᝅ⊂ᯕᇩa܆
eḲᵲශ8.78%ᮥaᰆฯᯕᱢᬊ⦹Łᯩ݅(Korea Development Institute, 2008).
ᅙᩑǍ᮹đŝෝá᷾⦹ʑ᭥⦹ᩍ, ᇥᕾᨱᦿᕽ݉ᙽᯥ᮹⢽ᅙ⇵
⇽(simple random sampling) ႊჶᮥ☖⧕⇵⇽ࡽ13}ᩎॅᮥݡ ᔢᮝಽbᩎ᮹ᯝ⠪Ɂ⦹₉ᯙᬱᙹᨱᅙᩑǍᨱᕽᱽࡽ℉ࢱ
eḲᵲශŝ⢽ᵡḡ⋉ᨱᱽࡽ℉ࢱeḲᵲශᮥbbŒ⦹ᩍ
℉ࢱeᯙᬱᙹෝᔑ⇽⧉ᮝಽ៉ᝅᱽ⊂ᱶࡽ℉ࢱeᯙᬱᙹ᪡
á᷾đŝෝᔕ⠕ᅕ໕, ᅙᩑǍᨱᕽᱽࡽđŝaᩩእ┡ݚᖒ᳑
ᔍ⢽ᵡḡ⋉ᨱእ⧕ᕽ᪅₉ᮉᯕ⭉ᦍᱢíӹ┡ӽäᮥ⪶ᯙ⧁
ᙹᯩ݅. ᩩእ┡ݚᖒ᳑ᔍ⢽ᵡḡ⋉᮹℉ࢱeḲᵲශsᮥᯝ⠪Ɂ
~ᙹ᪡Œ⦹ᩍ℉ࢱe~ᙹෝ⇵ᱶ⧕ᅕ໕, ᝅ⊂sŝᱢí۵
᧞2%ᨱᕽฯí۵᧞30%ʭḡ᮹᪅₉aၽᔾ⦹ᩡ݅. ᯕᨱእ⧕
ᅙᩑǍᨱᕽᔑ⇽ࡽ℉ࢱeḲᵲශᮥ☖⧕℉ࢱe~ᙹෝ
⇵ᱶ⧁Ğᬑ, ᱢí۵᧞0.5%ᨱᕽฯí۵15%ʭḡ᮹᪅₉aၽᔾ
⦹ᩡ݅.
✚⯩⢽ᵡḡ⋉᮹sᮥᱢᬊ⦽Ğᬑə൚1, ᷪᵝÑḡᩎ᮹ᩎᔍᨱ
ᱢᬊ⦹ᩡᮥভ᪅₉aⓍíၽᔾ⦹ᩡ۵ߑᯕෝ☖⧕ᩩእ┡ݚᖒ᳑
ᔍ ⢽ᵡḡ⋉᮹ ℉ࢱe Ḳᵲශ 8.78%a ᔢݡᱢᮝಽ ׳ᮡ
℉ࢱeḲᵲශᮥӹ┡ԕ۵ᵝÑḡᩎ᮹ࠥ℁ࠥᩎ᮹ᙹ✚ᖒ
ᮥ ᱽݡಽ ၹᩢ⦹ḡ ༜⦽݅Ł ั⧁ ᙹ ᯩ݅. ᕽ ᅙ ᩑǍ᮹
đŝ۵ḡ⋉ᨱᱽࡽsᨱእƱ⦹ᩍ᳡⩥ᝅᮥᱶ⪶⦹íၹᩢ⦽
⧊ญᱢᯙ sᮥ ᱽ⧩݅Ł ⠪a⧁ ᙹ ᯩ݅.
6. đುၰ⨆⬥ᩑǍŝᱽ
ᅙ ᩑǍᨱᕽ۵ ᯙᇥᕾ ၰ ǑḲᇥᕾ ʑჶᮥ ⪽ᬊ⦹ᩍ ᝁȽ
ᩎᔍᨱݡ⦽℉ࢱeḲᵲශᮥᔑᱶ⦹۵ႊჶುᮥᱽ⦹ᩡ݅.
ᯕෝ᭥⧕ᕽᬙ✚ᄥෝݡᔢᮝಽࠥ℁ࠥᩎᄥ24e⦹₉
ᯙᬱᙹ⊂ᱶᯱഭၰ ᙹ⊹ḡᱢࠥ᪡Õ⇶ྜྷݡᰆᯱഭෝၵ┶ᮝಽ
ᇥᕾᮥ ᝅ⦹ᩡ݅. ᵝ ᩑǍđŝෝ ᧞⦹໕ ݅ᮭŝ z݅.
ℌṙ, bᩎᄥ℉ࢱeḲᵲශᨱšಉᯕᯩ۵ᯙᮥ⇵⇽⦹ʑ
᭥⧕ᕽ5aḡ~ᙹ➉▕ᄡᙹ᪡4aḡ☁ḡᯕᬊᄡᙹෝᖅᱶ⦹
ᩡ݅. ࢹṙ, ᯙᇥᕾŝǑḲᇥᕾᮥᙹ⧪⦹ᩍᱥℕࠥ℁ࠥᩎᮥ
3 }᮹ə൚ᮝಽᇥඹ⦹ᩡ݅. [ə൚1]ᮡᵝÑḡᩎ, [ə൚2]۵ᔢᨦḡ ᩎ, [ə൚3]ᮡᨦྕḡᩎ᮹✚ᖒᮥaḡŁᯩ۵äᮝಽ❱݉ࡽ݅.
ᖬṙ, ℉ࢱeḲᵲශᮡᵝÑḡᩎᯕ12.10%, ᔢᨦḡᩎᯕ8.45%, ᨦྕḡᩎᯕ10.76% ᙹᵡᯙäᮝಽᇥᕾࡹᨩ݅. ֘ṙ, ᔩíÕᖅ
ࡹ۵ ᩎᨱ ᱢᬊࢁ ℉ࢱe Ḳᵲශᮥ đᱶ⦹ʑ ᭥⧕ᕽ ‘ᯝ⠪Ɂ
~ᙹ’᪡‘ᨦྕLQḡᙹ’ෝ⪽ᬊ⦽ə൚⧁ݚŝᱶᮥᱽ⦹ᩡ݅.
݅ᖐṙ, ᇥᕾđŝ᮹á᷾ᮥ᭥⦹ᩍᯥ᮹⇵⇽⦽13}ᩎᔍᨱݡ⦹ᩍ
⢽ᵡḡ⋉ŝᅙᩑǍᨱᕽᱽࡽ℉ࢱeḲᵲශᮥbbᱢᬊ⦹ᩍ
ᝅ⊂⊹᪡እƱ⦹ᩡ݅. እƱđŝᅙᩑǍᨱᕽᱽࡽsᮥᔍᬊ⧩ᮥ
Ğᬑ᪅₉aᮁ᮹⦹í}ᖁࡹᨩ݅. ᕽᅙᩑǍ۵ʑ᳕᮹ႊჶು
ᨱ እ⧕ ᱶၡ⦹Ł ⧊ญᱢᯙ ႊჶುᯕ ⧁ ᙹ ᯩ݅.
ᅙᩑǍ᮹đŝෝ☖⧕ᕽᝁȽᩎᔍÕᖅ, ᩎᔍ✚ᖒᨱ฿۵
sᮥᱢᬊ⦹۵ᅙᩑǍ᮹ႊჶುᮥᱢᬊ⧁⦥aᯩᮭᮥᅕᩡ݅.
ݡࠥǭᨱᕽᔩíࠥ℁ࠥᩎᮥÕᖅ⧁ভ, ࠥ℁ࠥᩎ᮹℉ࢱ
eḲᵲශᮡ⧕ݚᩎ᮹ᯝ⠪Ɂ~ᙹᩩ⊂sŝᩎᖙǭ᮹ᨦྕLQ ḡᙹෝ⪶ᯙ⦹ᩍə൚ᄥಽ ℉ࢱeḲᵲශᮥ݅íᱢᬊ⦹۵
äᯕ ⧊ญᱢᯕŁ ❱݉ࡽ݅.
⨆⬥ᩑǍŝᱽಽᕽ, ᕽᬙᯕ᮹݅᧲⦽ḡᩎᮥݡᔢᮝಽᅙ
